code: def gumbel(
      random_state,
      loc=0.0,
      scale=1.0,
      size=None,
      chunk_size=None,
      gpu=None,
      dtype=None,
  ):
      r"""
      Draw samples from a Gumbel distribution.
  
      Draw samples from a Gumbel distribution with specified location and
      scale.  For more information on the Gumbel distribution, see
      Notes and References below.
  
      Parameters
      ----------
      loc : float or array_like of floats, optional
          The location of the mode of the distribution. Default is 0.
      scale : float or array_like of floats, optional
          The scale parameter of the distribution. Default is 1.
      size : int or tuple of ints, optional
          Output shape.  If the given shape is, e.g., ``(m, n, k)``, then
          ``m * n * k`` samples are drawn.  If size is ``None`` (default),
          a single value is returned if ``loc`` and ``scale`` are both scalars.
          Otherwise, ``np.broadcast(loc, scale).size`` samples are drawn.
      chunk_size : int or tuple of int or tuple of ints, optional
          Desired chunk size on each dimension
      gpu : bool, optional
          Allocate the tensor on GPU if True, False as default
      dtype : data-type, optional
        Data-type of the returned tensor.
  
      Returns
      -------
      out : Tensor or scalar
          Drawn samples from the parameterized Gumbel distribution.
  
      See Also
      --------
      scipy.stats.gumbel_l
      scipy.stats.gumbel_r
      scipy.stats.genextreme
      weibull
  
      Notes
      -----
      The Gumbel (or Smallest Extreme Value (SEV) or the Smallest Extreme
      Value Type I) distribution is one of a class of Generalized Extreme
      Value (GEV) distributions used in modeling extreme value problems.
      The Gumbel is a special case of the Extreme Value Type I distribution
      for maximums from distributions with "exponential-like" tails.
  
      The probability density for the Gumbel distribution is
  
      .. math:: p(x) = \frac{e^{-(x - \mu)/ \beta}}{\beta} e^{ -e^{-(x - \mu)/
                \beta}},
  
      where :math:`\mu` is the mode, a location parameter, and
      :math:`\beta` is the scale parameter.
  
      The Gumbel (named for German mathematician Emil Julius Gumbel) was used
      very early in the hydrology literature, for modeling the occurrence of
      flood events. It is also used for modeling maximum wind speed and
      rainfall rates.  It is a "fat-tailed" distribution - the probability of
      an event in the tail of the distribution is larger than if one used a
      Gaussian, hence the surprisingly frequent occurrence of 100-year
      floods. Floods were initially modeled as a Gaussian process, which
      underestimated the frequency of extreme events.
  
      It is one of a class of extreme value distributions, the Generalized
      Extreme Value (GEV) distributions, which also includes the Weibull and
      Frechet.
  
      The function has a mean of :math:`\mu + 0.57721\beta` and a variance
      of :math:`\frac{\pi^2}{6}\beta^2`.
  
      References
      ----------
      .. [1] Gumbel, E. J., "Statistics of Extremes,"
             New York: Columbia University Press, 1958.
      .. [2] Reiss, R.-D. and Thomas, M., "Statistical Analysis of Extreme
             Values from Insurance, Finance, Hydrology and Other Fields,"
             Basel: Birkhauser Verlag, 2001.
  
      Examples
      --------
      Draw samples from the distribution:
  
      >>> import mars.tensor as mt
  
      >>> mu, beta = 0, 0.1 # location and scale
      >>> s = mt.random.gumbel(mu, beta, 1000).execute()
  
      Display the histogram of the samples, along with
      the probability density function:
  
      >>> import matplotlib.pyplot as plt
      >>> import numpy as np
      >>> count, bins, ignored = plt.hist(s, 30, normed=True)
      >>> plt.plot(bins, (1/beta)*np.exp(-(bins - mu)/beta)
      ...          * np.exp( -np.exp( -(bins - mu) /beta) ),
      ...          linewidth=2, color='r')
      >>> plt.show()
  
      Show how an extreme value distribution can arise from a Gaussian process
      and compare to a Gaussian:
  
      >>> means = []
      >>> maxima = []
      >>> for i in range(0,1000) :
      ...    a = mt.random.normal(mu, beta, 1000)
      ...    means.append(a.mean().execute())
      ...    maxima.append(a.max().execute())
      >>> count, bins, ignored = plt.hist(maxima, 30, normed=True)
      >>> beta = mt.std(maxima) * mt.sqrt(6) / mt.pi
      >>> mu = mt.mean(maxima) - 0.57721*beta
      >>> plt.plot(bins, ((1/beta)*mt.exp(-(bins - mu)/beta)
      ...          * mt.exp(-mt.exp(-(bins - mu)/beta))).execute(),
      ...          linewidth=2, color='r')
      >>> plt.plot(bins, (1/(beta * mt.sqrt(2 * mt.pi))
      ...          * mt.exp(-(bins - mu)**2 / (2 * beta**2))).execute(),
      ...          linewidth=2, color='g')
      >>> plt.show()
      """
      if dtype is None:
          dtype = (
              np.random.RandomState()
              .gumbel(handle_array(loc), handle_array(scale), size=(0,))
              .dtype
          )
      size = random_state._handle_size(size)
      seed = gen_random_seeds(1, random_state.to_numpy())[0]
      op = TensorGumbel(seed=seed, size=size, gpu=gpu, dtype=dtype)
      return op(loc, scale, chunk_size=chunk_size)
  
file_path: mars/mars/tensor/random/gumbel.py
imports: import numpy
  from ...None import opcodes
  from ...serialization.serializables import AnyField
  from ..utils import gen_random_seeds
  from .core import TensorRandomOperandMixin, handle_array, TensorDistribution
label_desc: Use image node_id 1 for calling a global function with example usage: gumbel(random_state, loc, scale, size, chunk_size, gpu, dtype) and returns: op